{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Quantum Key Distribution (BB84 Protocol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Let's say **Alice 💁‍♀️ and Bob 🙋‍♂️** want to set up a secure communication channel, so that they can send private messages to each other without worrying about someone eavesdropping on their conversation.\n",
    "Unfortunately, it is virtually impossible to set up such a secure channel—there would always be some way of tapping into it and reading their messages.\n",
    "\n",
    "That's where cryptography comes in! If Alice wants to send a message to Bob, she can encrypt it in some way (say, by replacing every letter in the alphabet with a number). \n",
    "\n",
    "$$\n",
    "A \\rightarrow 1 \\\\\n",
    "B \\rightarrow 2 \\\\\n",
    "C \\rightarrow 3 \\\\\n",
    "\\vdots \\\\\n",
    "Z \\rightarrow 26\n",
    "$$\n",
    "\n",
    "If Bob knows exactly how she encoded the message, he can decode it by reversing the encryption (replace every 1 with an $A$, every 2 with a $B$, etc.) However, if **Eve 😈** (or any other eavesdropper) were to  hack their communication channel, they would still have no idea what the message says! It would just look like a string of numbers to them!\n",
    "\n",
    "Of course, an encryption scheme like this would be pretty easy to crack—Eve could just guess that the numbers correspond to certain letters of the alphabet—but Alice and Bob could always use a more advanced technique instead. Many of these encryption techniques fall under the category of **Symmetric Key Cryptography**, in which each party is given a *key* 🔑 which contains the instructions for encrypting and decrypting the message. In our earlier example, this key would have contained the instructions to replace every letter of the alphabet with it's corresponding index. This key could just be a string of bits (0s and 1s) encoding the instructions for encryption and decryption.\n",
    "\n",
    "Let's assume that, as long as Alice and Bob share a secret key, they are able to securely encrypt their messages in a way that would be impossible to decrypt without this key. So, if Eve taps into their communication channel and reads their messages, she would have no idea what the messages say, since she would not know the key used to encrypt and decrypt them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is BB84?\n",
    "Of course, in order to actually set up this encrypted channel, Alice and Bob actually need to communicate in secret to decide what key to use. But this kind of secure communication requires an encrypted channel, which is what we're trying to create in the first place!\n",
    "\n",
    "Ideally, Alice and Bob could meet up in the real world, and Alice could whisper the key into Bob's ear. But it would be impractical to meet up everytime they wanted to setup a private message channel—imagine if you had to meet someone in person before being able to email them at all!\n",
    "\n",
    "The BB84 protocol is a method of Quantum Key Distribution which solves this dilemma. If Alice uses this protocol to send a key to Bob, then they can know, with near-perfect certainty, whether the key has been intercepted or not. If it is *not* intercepted, have a secret key that they can use to set up an encrypted channel, so they can communicate freely without worrying about their messages being read by someone else! However, if the key *is* intercepted, they can just repeat the process again and again until they have a key that no one else knows.\n",
    "\n",
    "However, there are certain requirements for this protocol to work:\n",
    "* Both Alice and Bob must have access to their own quantum computer.\n",
    "* They must have a communication channel capable of transmitting qubits. This could be some kind of fibre-optic cable capable of transmitting polarized photons.\n",
    "* They must have a classical communication channel (like a telephone cable).\n",
    "Since it is impossible to ensure perfect security, we must assume that any of these channels can be tapped into by Eve the Eavesdropper 😈."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "Here's how the protocol works in a nutshell:\n",
    "1. Alice creates a random string of bits, and for each bit, she randomly chooses a basis to encode it in.\n",
    "2. Alice encodes the bits into qubits using her chosen bases, and sends the qubits over a quantum communication channel to Bob's quantum computer.\n",
    "3. Bob also randomly chooses a basis to decode each qubit in. He measures each qubit in the bases he chose.\n",
    "4. Alice uses a classical communication channel to tell Bob which bases she chose. She also tells him the first few bits she sent.\n",
    "5. Bob analyzes these first few bits to determine whether Eve tapped into their quantum communication channel and intercepted Alice's qubits.\n",
    "6. If Eve did *not* intercept the qubits, they consider all of the qubits that they happened to choose the same bases for, and use those bits as their key. If Eve *did* intercept the qubits, they repeat the process all over again.\n",
    "\n",
    "Now, let's break down what each of those steps entails. **First, we'll take a look at how it works if the message is not intercepted by Eve**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Qiskit\n",
    "from qiskit import *\n",
    "from qiskit.tools.visualization import plot_bloch_multivector\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding\n",
    "The first step is for Alice to choose a string of bits and bases to encode them in. What do we mean by \"bases\"?\n",
    "Well, we can represent a qubit as a vector on a \"Bloch Sphere\". Each axis can be considered a possible bases. If a vector points directly upward, it is said to be in the state $|0\\rangle$, and if it points downward, it is said to be in the state $|1\\rangle$. The vertical axis is called the $Z$-axis. In the $Z$-basis, we can encode a 0 as $|0\\rangle$ (shown in the image on the left) and a 1 as $|1\\rangle$ (shown in the image on the right)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ignore this code. This is just used to generate the image mentioned in the previous paragraph.\n",
    "qc = QuantumCircuit(2)\n",
    "qc.x(1)\n",
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "result = execute(qc, backend=simulator).result()\n",
    "statevector = result.get_statevector()\n",
    "plot_bloch_multivector(statevector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another basis we can use is the $X$-basis, which lies on the $X$-axis. Here, the image on the left represents the $|+\\rangle$ state, which can represent a bit value of 0, and the image on the right represents the $|-\\rangle$ state, which can represent a bit value of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ignore this code. This is just used to generate the image mentioned in the previous paragraph.\n",
    "qc = QuantumCircuit(2)\n",
    "qc.h(0)\n",
    "qc.x(1)\n",
    "qc.h(1)\n",
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "result = execute(qc, backend=simulator).result()\n",
    "statevector = result.get_statevector()\n",
    "plot_bloch_multivector(statevector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have two different ways to encode a 0, and two different ways to encode a 1!\n",
    "\n",
    "The first step is for Alice to randomly create a string of, say, 500 bits. She can do this by flipping a coin and writing down 0 everytime she lands on heads and 1 everytime she lands on tails."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparation for encoding\n",
    "KEY_LENGTH = 500\n",
    "random.seed(0) # Seed the random number generator. This will be used as our \"coin flipper\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The bits Alice is going to send are: 1101111110...\n"
     ]
    }
   ],
   "source": [
    "# Generating a random string of bits\n",
    "alice_bits = \"\"\n",
    "for i in range(KEY_LENGTH):\n",
    "    randBit = random.randint(0, 1) # Flip Coin\n",
    "    alice_bits += str(randBit) # Add randomly chosen bit to the bit string.\n",
    "    \n",
    "print(\"The bits Alice is going to send are: \" + alice_bits[:10] + \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, Alice randomly chooses a basis of each bit (either the $Z$-basis or the $X$-basis). She can do this by flipping a coin and writing down $Z$ everytime she lands on heads and $X$ everytime she lands on tails."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_random_bases(num_of_bases):\n",
    "    \"\"\"This function selects a random basis for each bit\"\"\"\n",
    "    bases_string = \"\"\n",
    "    for i in range(num_of_bases):\n",
    "        randBasis = random.randint(0, 1) # Flip Coin\n",
    "\n",
    "        if randBasis == 0:\n",
    "            bases_string += \"Z\" \n",
    "        else:\n",
    "            bases_string += \"X\"\n",
    "            \n",
    "    return bases_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The bases Alice is going to encode them in are: ZXXZZZZXZX...\n"
     ]
    }
   ],
   "source": [
    "alice_bases = generate_random_bases(KEY_LENGTH) # Alice randomly chooses a basis for each bit.\n",
    "    \n",
    "print(\"The bases Alice is going to encode them in are: \" + alice_bases[:10] + \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, Alice encodes each bit into its corresponding basis on her quantum computer, creating a string of 500 qubits. She sends these qubits over an optical cable to Bob. Let's see how to do this using Qiskit.\n",
    "\n",
    "By default, IBM's quantum computers measure in the $Z$-basis, and all qubits are initialized to $|0\\rangle$. To turn this into a $|1\\rangle$, we need to apply an $X$ gate.\n",
    "\n",
    "To encode in the $X$-basis, we start with the corresponding $|0\\rangle$ or $|1\\rangle$, and then apply a Hadamard (H) gate. This gate converts a $|0\\rangle$ into a $|+\\rangle$ and a $|1\\rangle$ into a $|-\\rangle$.\n",
    "\n",
    "| Bit | Basis | Qubit State | Gate Required |\n",
    "|:---:|:-----:|:-----------:|:-------------:|\n",
    "| 0 | Z | $\\vert0\\rangle$ | None |\n",
    "| 1 | Z | $\\vert1\\rangle$ | X |\n",
    "| 0 | X | $\\vert+\\rangle$ | H |\n",
    "| 1 | X | $\\vert-\\rangle$ | X then H |\n",
    "\n",
    "We'll store the quantum circuit used to encode each qubit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode(bits, bases):\n",
    "    \"\"\"This function encodes each bit into the given basis.\"\"\"\n",
    "    \n",
    "    encoded_qubits = []\n",
    "    \n",
    "    for bit, basis in zip(bits, bases):\n",
    "        qc = QuantumCircuit(1, 1) # Create a quantum circuit for each qubit\n",
    "        \n",
    "        # Possible Cases\n",
    "        if bit==\"0\" and basis == \"Z\":\n",
    "            encoded_qubits.append(qc) # Do not apply any gates\n",
    "\n",
    "        elif bit==\"1\" and basis == \"Z\":\n",
    "            qc.x(0) # Apply X Gate\n",
    "            encoded_qubits.append(qc)\n",
    "\n",
    "        elif bit==\"0\" and basis == \"X\":\n",
    "            qc.h(0) # Apply H Gate\n",
    "            encoded_qubits.append(qc)\n",
    "\n",
    "        elif bit==\"1\" and basis == \"X\":\n",
    "            qc.x(0) # Apply X Gate\n",
    "            qc.h(0) # Apply H Gate\n",
    "            encoded_qubits.append(qc)\n",
    "            \n",
    "    return (encoded_qubits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ┌───┐\n",
      "q_0: ┤ X ├\n",
      "     └───┘\n",
      "c: 1/═════\n",
      "          \n",
      "     ┌───┐┌───┐\n",
      "q_0: ┤ X ├┤ H ├\n",
      "     └───┘└───┘\n",
      "c: 1/══════════\n",
      "               \n",
      "     ┌───┐\n",
      "q_0: ┤ H ├\n",
      "     └───┘\n",
      "c: 1/═════\n",
      "          \n",
      "     ┌───┐\n",
      "q_0: ┤ X ├\n",
      "     └───┘\n",
      "c: 1/═════\n",
      "          \n",
      "     ┌───┐\n",
      "q_0: ┤ X ├\n",
      "     └───┘\n",
      "c: 1/═════\n",
      "          \n",
      "etc.\n"
     ]
    }
   ],
   "source": [
    "# Encode Alice's bits\n",
    "encoded_qubits = encode(alice_bits, alice_bases)\n",
    "\n",
    "# Print circuits for first 5 qubits.\n",
    "for i in range(5):\n",
    "    print(encoded_qubits[i])\n",
    "print(\"etc.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sending the qubits\n",
    "Normally, Alice would send these encoded qubits over a quantum channel, such as an optical fibre cable. However, since we don't have quantum channels in the real world yet, we'll store the circuits for each qubit in an array called `QUANTUM_CHANNEL` for the sake of demonstration. This would be like if Alice stored the qubits in a building and waited for Bob to pick them up. Of course, just like a fibre optic cable, this building is also a location where Eve might intercept the message."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "QUANTUM_CHANNEL = encoded_qubits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measurement\n",
    "The next step is for Bob to receive the encoded qubits and measure them. First, he must choose his own set of random bases, just like Alice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The bases Bob is going to decode them in are: XZZXZZZXXX...\n"
     ]
    }
   ],
   "source": [
    "bob_bases = generate_random_bases(KEY_LENGTH) # Bob randomly chooses a basis for each bit.\n",
    "    \n",
    "print(\"The bases Bob is going to decode them in are: \" + bob_bases[:10] + \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, Bob must measure each Qubit in the corresponding bases he chose. In Qiskit, this can be accomplished by adding a measurement gate to the circuit for each encoded qubit, and then executing it. However, IBM's quantum computers measure in the $Z$-basis by default, so to measure in the $X$-basis, we must apply a Hadamard gate before our measurement gate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure(qubits, bases):\n",
    "        \"\"\"This function measures each qubit in the corresponding basis chosen for it.\"\"\"\n",
    "\n",
    "        bits = \"\" # The results of measurements\n",
    "\n",
    "        for qubit, basis in zip(qubits, bases):\n",
    "\n",
    "            # Add measurement depending on basis\n",
    "            if basis == \"Z\":\n",
    "                qubit.measure(0, 0)\n",
    "            elif basis == \"X\":\n",
    "                qubit.h(0)\n",
    "                qubit.measure(0, 0)\n",
    "\n",
    "            # Execute on Simulator\n",
    "            simulator = Aer.get_backend('qasm_simulator')\n",
    "            result = execute(qubit, backend=simulator, shots=1).result()\n",
    "            counts = result.get_counts()\n",
    "            measured_bit = max(counts, key=counts.get) # Max doesn't matter for simulator since there is only one shot.\n",
    "\n",
    "            bits += measured_bit\n",
    "            \n",
    "        return bits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first few bits Bob received are: 0000111100...\n"
     ]
    }
   ],
   "source": [
    "qubits_received = QUANTUM_CHANNEL # Receive qubits from quantum channel\n",
    "bob_bits = measure(qubits_received, bob_bases)\n",
    "\n",
    "print(\"The first few bits Bob received are: \" + bob_bits[:10] + \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison\n",
    "Now, Alice needs to tell Bob which bases she chose to encode her qubits in. She can tell him over any classical channel. The beauty of this protocol is that it doesn't matter if Eve finds out which bases Alice used. Alice could even publicly post the list of bases she used on Twitter!\n",
    "\n",
    "![Alice can even announce it on Twitter!](images/AnnounceBases.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLASSICAL_CHANNEL = alice_bases # Alice tells Bob which bases she used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of the qubits where Alice and Bob chose different bases, there is a 50% chance Bob's measurement returned the wrong qubit. For example, If Alice sent Bob a qubit in the $|+\\rangle$ state (i.e., a bit value of 0 encoded in the $X$-basis), and Bob measures in the $Z$-basis, there is a 50% chance he will get a $|0\\rangle$ and a 50% chance he will get a $|1\\rangle$. Thus, every instance where their bases don't match is useless to them, so Bob needs to find the bases they share in common."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The indices of the first 10 bases they share in common are: [4, 5, 6, 7, 9, 10, 11, 19, 22, 24]\n"
     ]
    }
   ],
   "source": [
    "# Store the indices of the bases they share in common\n",
    "common_bases = [i for i in range(KEY_LENGTH) if CLASSICAL_CHANNEL[i]==bob_bases[i]]\n",
    "\n",
    "print(\"The indices of the first 10 bases they share in common are: \" + str(common_bases[:10]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that Bob knows the bases they share in common, he can discard all the rest of the bits, and only keep the ones that were measured in the same bases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "bob_bits = [bob_bits[index] for index in common_bases]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "He also tells Alice which bases they had in common, so that she can discard the rest of the bits as well, keeping only the bits that were measured in the same bases that she encoded them in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLASSICAL_CHANNEL = common_bases # Bob tells Alice which bases they shared in common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "alice_bits = [alice_bits[index] for index in common_bases] # Alice keeps only the bits they shared in common"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Alice and Bob are only keeping the bits measured in the bases they shared in common, they *should* have the same bits. To make sure this is the case, Alice will announce the first few bits that she has, and Bob should have the same ones. Of course, if Eve were trying to eavesdrop, she would also hear these first few bits, so Alice and Bob would have to discard them as well (after comparing to make sure they're the same as what they expect)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yep, Alice and Bob seem to have the same bits!\n"
     ]
    }
   ],
   "source": [
    "CLASSICAL_CHANNEL = alice_bits[:100] # Alice tells Bob the first 100 bits she has left.\n",
    "\n",
    "# Bob checks if they match the first 100 bits that he has\n",
    "if CLASSICAL_CHANNEL == bob_bits[:100]:\n",
    "    print(\"Yep, Alice and Bob seem to have the same bits!\")\n",
    "else:\n",
    "    print(\"Uh oh, at least one of the bits is different.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the first 100 bits are the same, Alice and Bob can be fairly certain that the remaining bits also match. Now, they need to discard the first 100 bits, since Eve may have been listening in on the classical channel and keeping track of what they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "alice_bits = alice_bits[100:] # Alice discards the first 100 bits\n",
    "bob_bits = bob_bits[100:] # Alice discards the first 100 bits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These remaining bits are the 🔑 key that Alice and Bob need to set up an encrypted communication channel! Now they can communicate without worrying about their messages being read by someone else!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shhhhh, the key is:\n",
      "010101101001011110110100011011001100000111010101000101011101001000110101100111101001101010000111000111000011001110100100000\n",
      "Don't tell anyone!\n",
      "\n",
      "The key is 123 bits long.\n"
     ]
    }
   ],
   "source": [
    "key = \"\" \n",
    "for bit in alice_bits: # Or bob_bits, since both should be the same\n",
    "    key += bit\n",
    "\n",
    "print(\"Shhhhh, the key is:\")\n",
    "print(str(key))\n",
    "print(\"Don't tell anyone!\")\n",
    "\n",
    "print(\"\\nThe key is \" + str(len(key)) + \" bits long.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The length of the key varies depending on how many bases they share in common."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interception\n",
    "___\n",
    "So far, we've only looked at the case where no one spying on Alice and Bob. But what if someone like Eve tries to intercept their key? Let's see what happens in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, Alice performs the encoding step as usual: she randomly generates a string of bits and bases to encode them in. She then sends the encoded qubits along a quantum channel (fibre-optic cable) to Bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating a random string of bits\n",
    "alice_bits = \"\"\n",
    "for i in range(KEY_LENGTH):\n",
    "    randBit = random.randint(0, 1) # Flip Coin\n",
    "    alice_bits += str(randBit) # Add randomly chosen bit to the bit string.\n",
    "    \n",
    "# Alice randomly chooses a basis for each bit.\n",
    "alice_bases = generate_random_bases(KEY_LENGTH)\n",
    "\n",
    "# Encode Alice's bits\n",
    "encoded_qubits = encode(alice_bits, alice_bases)\n",
    "\n",
    "QUANTUM_CHANNEL = encoded_qubits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⚠️ Interception by Eve ⚠️\n",
    "This time, the qubits are intercepted by 😈 Eve. Let's she what she would do. First, she would randomly choose a set of bases to measure the qubits in (since she has no idea which bases Alice used). Then, she'll perform the measurements. This is similar to what Bob would normally do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "qubits_intercepted = QUANTUM_CHANNEL # Intercept qubits\n",
    "eve_bases = generate_random_bases(KEY_LENGTH) # Generate a random set of bases\n",
    "eve_bits = measure(qubits_intercepted, eve_bases) # Measure the qubits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the No-Cloning Theorem of Quantum Mechanics, Eve cannot just copy the qubits over from the quantum channel. Thus, Bob will never receive the qubits, making it obvious to him and Alice that their message was intercepted. To prevent them from realizing what has happened, Eve must create her own decoy qubits to send to Bob. Since she has no idea which bases Alice used, she has to generate the bases randomly. This is similar to what Alice originally did when she encoded the qubits. \n",
    "\n",
    "Eve might generate an entirely new set of bases, or she might just use the same ones she used to measure the qubits. For simplicity, let's assume the bases she used to intercept the qubits are the same bases she uses to encode her decoy qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eve encodes her decoy qubits and sends them along the quantum channel\n",
    "QUANTUM_CHANNEL = encode(eve_bits, eve_bases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bob then performs his usual steps: selecting a measurement bases and measuring the qubits. He doesn't know that Eve has intercepted them yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "bob_bases = generate_random_bases(KEY_LENGTH) # Bob randomly chooses a basis for each bit.\n",
    "qubits_received = QUANTUM_CHANNEL # Receive qubits from quantum channel\n",
    "bob_bits = measure(qubits_received, bob_bases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison\n",
    "Again, Alice needs to tells Bob which bases she chose to encode her qubits in. She can tells him over any classical channel (perhaps even publicly on Twitter). Since this classical channel is public, Eve also knows which bases she used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLASSICAL_CHANNEL = alice_bases # Alice tells Bob which bases she used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual, Bob checks which bases they share in common. He discards the bits that were not measured in the same bases and keeps the ones that were measured in the same bases Alice encoded them in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the indices of the bases they share in common\n",
    "common_bases = [i for i in range(KEY_LENGTH) if CLASSICAL_CHANNEL[i]==bob_bases[i]]\n",
    "bob_bits = [bob_bits[index] for index in common_bases]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "He then tells Alice the indices of the bases they shared in common so that she can do the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLASSICAL_CHANNEL = common_bases # Bob tells Alice which bases they shared in common\n",
    "alice_bits = [alice_bits[index] for index in common_bases] # Alice keeps only the bits they shared in common"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Waiiiiiiit a Second...\n",
    "Since Alice and Bob are only keeping the bits measured in the bases they shared in common, they *should* have the same bits. To make sure this is the case, Alice will announce the first few bits that she has, and Bob should have the same ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uh oh, at least one of the bits is different.\n"
     ]
    }
   ],
   "source": [
    "CLASSICAL_CHANNEL = alice_bits[:100] # Alice tells Bob the first 100 bits she has left.\n",
    "\n",
    "# Bob checks if they match the first 100 bits that he has\n",
    "if CLASSICAL_CHANNEL == bob_bits[:100]:\n",
    "    print(\"Yep, Alice and Bob seem to have the same bits!\")\n",
    "else:\n",
    "    print(\"Uh oh, at least one of the bits is different.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After comparing the first 100 bits, they see that their bits don't match! Assuming that their is no noise in the quantum channel and quantum computers (which could cause errors), Alice and Bob can be certain that their message was intercepted! Thus, they can throw away all their bits and repeat the same protocol all over again. This time, they might use a different quantum channel in an effort to throw Eve off!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Sources\n",
    "* Qiskit Textbook: https://qiskit.org/textbook/ch-algorithms/quantum-key-distribution.html\n",
    "* Qubit by Qubit's Introduction to Quantum Computing Course"
   ]
  }
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